Package: sparsestep
Version: 1.0.1
Date: 2021-01-10
Title: SparseStep Regression
Authors@R: c(person("Gertjan", "van den Burg", role=c("aut", "cre"), 
                    email="gertjanvandenburg@gmail.com"), 
              person("Patrick", "Groenen", email="groenen@ese.eur.nl", role="ctb"), 
              person("Andreas", "Alfons", email="alfons@ese.eur.nl", role="ctb"))
Description: Implements the SparseStep model for solving regression
    problems with a sparsity constraint on the parameters. The SparseStep
    regression model was proposed in Van den Burg, Groenen, and Alfons (2017)
    <arXiv:1701.06967>. In the model, a regularization term is added to the
    regression problem which approximates the counting norm of the parameters.
    By iteratively improving the approximation a sparse solution to the
    regression problem can be obtained.  In this package both the standard
    SparseStep algorithm is implemented as well as a path algorithm which uses
    golden section search to determine solutions with different values for the
    regularization parameter.
License: GPL (>= 2)
Imports: graphics
Depends:
    R (>= 3.0.0),
    Matrix (>= 1.0-6)
Classification/MSC: 62J05, 62J07
URL: https://github.com/GjjvdBurg/SparseStep,
     https://arxiv.org/abs/1701.06967
BugReports: https://github.com/GjjvdBurg/SparseStep
RoxygenNote: 7.1.0